Humans have natural abilities involving visual association and more readily recall photos by image content such as a face, scenery, an object such as a car or boat because the brain stores image information as well as, if not better than, text information. Often, a user remembers an image in a document but fails to recall the title of the image or much other information about the document wherein the image was last remembered to be contained within. Method for searching archived images and images within documents are known. Such methods typically associate one or more text strings with an image, such as “1967 Ford Mustang”, when the image is archived. The title of an image or sub-text for the image contained within the document is made searchable by an indexing technique. Subsequent text based searches for a matching image in the archival repository would try to return a best match for the query text string used to search for the corresponding image. If the text query used for the search is not correct and no match is found, the user must revise the query string in differing variations until a match is found or until the user determines that the image does not exist in the archive or it simply cannot be found.
Methods have arisen in the art which enable searching for matching images based on image content. However, complications arise from the fact that two images that appear identical may have different digital representations thus making it difficult for automated methods to locate matching images. The query image used for the search may have undergone incidental changes as a result of image processing performed on the image such as color and shading. The query image may have been cropped and/or resized. It may have been rotated, or generated with modified control parameters such as a higher or lower contrast ratios and the like. Content within the image may have been manually or electronically edited or removed. Generally, images go through several operations which tend to introduce distortional differences making the present image different from the original image. As such, the original image is now different from the version of the image contained in one of the document in the repository. Conversely, the query image may be in good shape or may be the original image and the distorted image is the one embedded in a document or stored in an image archive. Searching such an archive or a repository of image-bearing documents for a match between an original image and a distorted image is a difficult problem in this art.
Accordingly, what is needed in this art are increasingly sophisticated systems and methods for querying a repository of image-bearing documents using a source image as a query which is robust against perceptually acceptable distortional differences and which can be used as an adjunct to text-based search queries.